Sb- and Bi-doped Mg<sub>2</sub>Si: location of the dopants, micro- and nanostructures, electronic structures and thermoelectric properties
Bibliographic record
Abstract
Due to increasing global energy concerns, alternative sustainable methods to create energy such as thermoelectric energy conversion have become increasingly important. Originally, research into thermoelectric materials was focused on tellurides of bismuth and lead because of the exemplary thermoelectric properties of Bi2Te3 and PbTe. These materials, however, contain toxic lead and tellurium, which is also scarce and thus expensive. A viable alternative material may exist in Mg2Si, which needs to be doped and alloyed in order to achieve reasonable thermoelectric efficiency. Doping is a major problem, as p-type doping has thus far not produced competitive efficiencies, and n-type doping is problematic because of the low solubility of the typical dopants Sb and Bi. This investigation shows experimentally that these dopants can indeed replace Si in the crystal lattice, and excess Sb and Bi atoms are present in the grain boundaries in the form of Mg3Sb2 and Mg3Bi2. As a consequence, the carrier concentration is lower than the formal Sb/Bi concentration suggests, and the thermal conductivity is significantly reduced. DFT calculations are in good agreement with the experimental data, including the band gap and the Seebeck coefficient. Overall, this results in competitive efficiencies despite the low carrier concentration. While ball-milling was previously shown to enhance the solubility of the dopants and thus the carrier concentration, this did not lead to enhanced thermoelectric properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".